A classification model, i.e. a classifier, is built using machine learning technology, which may be generally divided into three phases: sample labeling, feature extraction and model training in a training phase. In the prior art, unlabeled training samples, i.e. data of unknown types, need to be manually labeled one by one, so as to obtain labeled training samples, i.e. data of known types, and then a large number of training samples of the known types may be used to built a classifier. The classifier may be applied in many scenarios, for example, since more and more spam pages are generated due to network fraud, which seriously impacts the retrieval efficiency of a search engine and the user experience, counteracting fraud has become one of the most important challenges faced by the search engine, and labeled normal data and fraud data may be used to built a classifier to identify network data. Regarding machine learning, the greater the number of training samples, the higher the classification accuracy rate of the built classifier, and a large number of known types of training samples need to be acquired.
However, the operation of manually labeling a large number of training samples is complex and prone to error, thereby resulting in the reduction of the efficiency and reliability of labeling the training samples.